





from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 							SCREAMING_SNAKE_CASE  (_UpperCAmelCase ):
   _UpperCamelCase       :						Union[str, Any]					   =			'''EncodecFeatureExtractor'''
   _UpperCamelCase       :						Tuple					   =			('''T5Tokenizer''', '''T5TokenizerFast''')


   def __init__(       self     :							Optional[Any]     ,	a     :							Optional[int]     ,	a     :							Optional[int]			)->  int:





     """simple docstring"""







     super().__init__(lowerCAmelCase__     ,	lowerCAmelCase__			)
     lowercase__           =    self.feature_extractor
     lowercase__           =    False


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[Any]     ,	a     :							Any=None     ,	a     :							Union[str, Any]=None     ,	a     :							Tuple=True			)->  Union[str, Any]:





     """simple docstring"""







     return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase__     ,	language=lowerCAmelCase__     ,	no_timestamps=lowerCAmelCase__			)


   def __call__(       self     :							List[Any]     ,	*a     :							Tuple     ,	**a     :							Optional[Any]			)->  Dict:





     """simple docstring"""







     if self._in_target_context_manager:
       return self.current_processor(*lowerCAmelCase__     ,	**lowerCAmelCase__			)

     lowercase__           =    kwargs.pop('audio'     ,	lowerCAmelCase__			)
     lowercase__           =    kwargs.pop('sampling_rate'     ,	lowerCAmelCase__			)
     lowercase__           =    kwargs.pop('text'     ,	lowerCAmelCase__			)
     if len(lowerCAmelCase__			) > 0:
       lowercase__           =    args[0]
       lowercase__           =    args[1:]

     if audio is None and text is None:
       raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

     if text is not None:
       lowercase__           =    self.tokenizer(lowerCAmelCase__     ,	**lowerCAmelCase__			)

     if audio is not None:
       lowercase__           =    self.feature_extractor(lowerCAmelCase__     ,	*lowerCAmelCase__     ,	sampling_rate=lowerCAmelCase__     ,	**lowerCAmelCase__			)

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

     else:
       lowercase__           =    audio_inputs['input_values']
       if "padding_mask" in audio_inputs:
         lowercase__           =    audio_inputs['padding_mask']
       return inputs


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Union[str, Any]     ,	*a     :							str     ,	**a     :							Union[str, Any]			)->  Any:





     """simple docstring"""







     lowercase__           =    kwargs.pop('audio'     ,	lowerCAmelCase__			)
     lowercase__           =    kwargs.pop('padding_mask'     ,	lowerCAmelCase__			)

     if len(lowerCAmelCase__			) > 0:
       lowercase__           =    args[0]
       lowercase__           =    args[1:]

     if audio_values is not None:
       return self._decode_audio(lowerCAmelCase__     ,	padding_mask=lowerCAmelCase__			)
     else:
       return self.tokenizer.batch_decode(*lowerCAmelCase__     ,	**lowerCAmelCase__			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[str]     ,	*a     :							Any     ,	**a     :							Union[str, Any]			)->  Tuple:





     """simple docstring"""







     return self.tokenizer.decode(*lowerCAmelCase__     ,	**lowerCAmelCase__			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							int     ,	a     :							str     ,	a     :							int = None			)->  List[np.ndarray]:





     """simple docstring"""







     lowercase__           =    to_numpy(lowerCAmelCase__			)
     lowercase__     ,       lowercase__     ,       lowercase__           =    audio_values.shape

     if padding_mask is None:
       return list(lowerCAmelCase__			)

     lowercase__           =    to_numpy(lowerCAmelCase__			)

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     lowercase__           =    seq_len - padding_mask.shape[-1]
     lowercase__           =    1 - self.feature_extractor.padding_value
     lowercase__           =    np.pad(lowerCAmelCase__     ,	((0, 0), (0, difference))     ,	'constant'     ,	constant_values=lowerCAmelCase__			)

     lowercase__           =    audio_values.tolist()
     for i in range(lowerCAmelCase__			):
       lowercase__           =    np.asarray(audio_values[i]			)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       lowercase__           =    sliced_audio.reshape(lowerCAmelCase__     ,	-1			)

     return audio_values



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)